"""simple docstring"""


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


a				=os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
a				='    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   =tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir      ,					"models/bert/" ) )
										lowerCamelCase__   =self.transformer_dir
										shutil.copy(
										    os.path.join(_lowerCamelCase      ,					"src/transformers/models/bert/modeling_bert.py" )      ,					os.path.join(self.transformer_dir      ,					"models/bert/modeling_bert.py" )      ,					)

				def 						_a      ( self ):
										lowerCamelCase__   ="src/transformers"
										shutil.rmtree(self.transformer_dir )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None ):
										lowerCamelCase__   =comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
										if overwrite_result is not None:
																lowerCamelCase__   =comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
										lowerCamelCase__   =black.Mode(target_versions={black.TargetVersion.PYaa}      ,					line_length=119 )
										lowerCamelCase__   =black.format_str(_lowerCamelCase      ,					mode=_lowerCamelCase )
										lowerCamelCase__   =os.path.join(self.transformer_dir      ,					"new_code.py" )
										with open(_lowerCamelCase      ,					"w"      ,					newline="\n" ) as f:
																f.write(_lowerCamelCase )
										if overwrite_result is None:
																self.assertTrue(len(check_copies.is_copy_consistent(_lowerCamelCase ) ) == 0 )
										else:
																check_copies.is_copy_consistent(f.name      ,					overwrite=_lowerCamelCase )
																with open(_lowerCamelCase      ,					"r" ) as f:
																						self.assertTrue(f.read()      ,					_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
										self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self ):
										# Base copy consistency
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"      ,					"BertLMPredictionHead"      ,					REFERENCE_CODE + "\n"      ,					)

										# With no empty line at the end
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"      ,					"BertLMPredictionHead"      ,					_lowerCamelCase      ,					)

										# Copy consistency with rename
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"      ,					"TestModelLMPredictionHead"      ,					re.sub("Bert"      ,					"TestModel"      ,					_lowerCamelCase )      ,					)

										# Copy consistency with a really long name
										lowerCamelCase__   ="TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
										self.check_copy_consistency(
										    F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''      ,					F'''{long_class_name}LMPredictionHead'''      ,					re.sub("Bert"      ,					_lowerCamelCase      ,					_lowerCamelCase )      ,					)

										# Copy consistency with overwrite
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"      ,					"TestModelLMPredictionHead"      ,					_lowerCamelCase      ,					overwrite_result=re.sub("Bert"      ,					"TestModel"      ,					_lowerCamelCase )      ,					)






				def 						_a      ( self ):
										lowerCamelCase__   =check_copies.LOCALIZED_READMES["README_zh-hans.md"]

										lowerCamelCase__   =(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
										    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
										    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
										    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
										    " Luong, Quoc V. Le, Christopher D. Manning."
										)
										lowerCamelCase__   =(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										lowerCamelCase__   =(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
										    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
										    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
										    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
										    " Christopher D. Manning 发布。\n"
										)

										lowerCamelCase__   , lowerCamelCase__   =check_copies.convert_to_localized_md(
										    _lowerCamelCase      ,					_lowerCamelCase      ,					localized_readme["format_model_list"] )

										self.assertFalse(_lowerCamelCase )
										self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =check_copies.convert_to_localized_md(
										    _lowerCamelCase      ,					_lowerCamelCase      ,					localized_readme["format_model_list"] )

										# Check whether the number of models is equal to README.md after conversion.
										self.assertTrue(_lowerCamelCase )

										lowerCamelCase__   =(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
										)
										lowerCamelCase__   =(
										    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
										    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										lowerCamelCase__   =(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)

										lowerCamelCase__   , lowerCamelCase__   =check_copies.convert_to_localized_md(
										    _lowerCamelCase      ,					_lowerCamelCase      ,					localized_readme["format_model_list"] )

										# Check if the model link is synchronized.
										self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )



"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


